In the minute it takes you to read this job description, Bluecore has launched over 100,000 individually personalized marketing campaigns for our retail e-commerce customers!

We are looking for Principal/Staff Data Scientists with a strong mathematical background to work alongside our Engineering Teams to build the next generation of retail and commerce models that delight and empower marketers.  The ideal candidate is one that has several years of experience researching, building, serving, and maintaining data science models at scale. They are able to work with our Product Team to translate product requirements into the correct objectives, perform literature searches to identify the right approach, design thoughtful experiments, and write production code to serve the model and maintain it.  They have first-hand experience with what works and what doesn’t, and are eager to share this experience with both junior and senior members and guide them through that process.  They are also able to, and excited to, help architect the data science infrastructure needed to accelerate innovation on models and facilitate serving and maintaining them. Finally, they should be curious and eager to identify and explore the myriad of other products that can be built on our unique data asset.  Our culture emphasizes making good tradeoffs, working as a team, and leaving your ego at the door.

Over the past six years Bluecore has shown that Marketing Teams can create meaningful and valuable experiences for their customers using only first party data; an increasingly important proposition with the rise in concerns around online privacy and third party data. As a Staff/Principal Data Scientist, you'll be joining a dedicated group of Data Scientists and Engineers at the forefront of exploring exciting ways to activate that data asset. We build powerful models that enable Marketers to make the right decisions and engage their customers with personalized content that is timely, relevant, and valuable.  Our approach to building models is an academic one, starting with a literature search, a baseline, and an iterative process of training and validation to identify the most suitable model that is as simple as possible and as powerful as necessary.  We employ a wide variety of models, such as Bayesian models for predicting customer lifetime value, matrix factorization to identify a customer’s product affinity, and reinforcement learning models to optimize content, timing, and frequency of marketing communications.

Our models operate at scale and crunch through millions of data points to make decisions that have been shown to double revenue and triple reach, and are designed in a flexible manner to generalize across our set of 400+ diverse customers who span industries from apparel to automotive.  To explore, build, deploy, and maintain models we leverage many tools such as BigQuery, Spark, Cloud SQL, Keras, TensorFlow, Airflow, Kubernetes, and Google Compute Engine. Finally, we’re a team that values applied research and constantly exploring the frontier of what’s possible; diving into fields such as topic modeling, restricted Boltzmann machines, recurrent neural networks, convolutional neural networks for image feature extraction, and differential privacy.

We are proud of the culture of flexibility, inclusivity and trust that we have built around our workforce. We are a remote first organization with the option to potentially work in our New York headquarters on occasion moving forward. We love the opportunity to come together – but employees will always have the option on where they work best.